A vision-based motion capture and recognition framework for behavior-based safety management

被引:257
作者
Han, SangUk [1 ]
Lee, SangHyun [2 ]
机构
[1] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA
[2] Univ Michigan, Dept Civil & Environm Engn, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
Safety; Behavior-based safety analysis; Vision-based tracking; Motion capture; Motion recognition; IMPROVING SAFETY; WORK; CONSTRUCTION; PERFORMANCE; EXPOSURE;
D O I
10.1016/j.autcon.2013.05.001
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In construction, about 80%-90% of accidents are associated with workers' unsafe acts. Nevertheless, the measurement of workers' behavior has not been actively applied in practice, due to the difficulties in observing workers on jobsites. In an effort to provide a robust and automated means for worker observation, this paper proposes a framework of vision-based unsafe action detection for behavior monitoring. The framework consists of (1) the identification of critical unsafe behavior, (2) the collection of relevant motion templates and site videos, (3) the 3D skeleton extraction from the videos, and (4) the detection of unsafe actions using the motion templates and skeleton models. For a proof of concept, experimental studies are undertaken to detect unsafe actions during ladder climbing (i.e., reaching far to a side) in motion datasets extracted from videos. The result indicates that the proposed framework can potentially perform well at detecting predefined unsafe actions in videos. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:131 / 141
页数:11
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